Method for image recombination of a plurality of images and image identification and system for image acquiring and identification

ABSTRACT

The present invention provides a method for image recombination of a plurality of images and image identification and a system for image acquiring and identification. Features with respect to the plurality of images are recombined and enhanced so as to form a recombined image. After that, the recombined image is processed to emphasize the features of the recombined image so that the recombined image is capable of being identified easily. Furthermore, the present provides a system to perform the foregoing method, whereby reducing unidentified problems caused due to low quality image of the monitoring system.

FIELD OF THE INVENTION

The present invention generally relates to an image identificationtechnology and, more particularly, to a method for image recombinationof a plurality of images and image identification and a system for imageacquiring and identification, wherein features with respect to theplurality of images are recombined so as to identify the content of animage.

BACKGROUND OF THE INVENTION

There are many people who get killed in traffic accidents. Theft andburglary using cars/motorcycles have been repeatedly reported. These maybe attributed to poor image identification of license plates because ofpoor monitoring systems. Such monitoring systems are mostly problematicbecause of poor resolution (320×240 Pixels) and slant angles of theimage acquiring units to cause blur or imcomplete images that cannot berecognized so that the criminals can be at large.

Conventionally, in Taiwan Patent No. 197752, a CCD camera and an imageacquiring unit are used to acquire a car image in the car lane and thecar image is then read by an image reading unit. Then, a logarithmicgreyscale operation unit is used to calculate the logarithmic greyscaleof each pixel in the car image. The image corresponding to thelogarithmic greyscales is decomposed by a wavelet decomposition unitinto rough images, horizontally differentiated images, verticallydifferentiated images and diagonally differentiated images. An imagebinarization unit converts the logarithmic greyscale of each pixel inthe horizontally differentiated images from real numbers into binarydigits 0 and 1. A rough image dividing unit determines a region with thehighest sum of binary digits within the whole car image according apre-set license plate size and thus the region is initially referred toas a license plate region. Then, a license plate slantness correctionunit corrects the slantness of the image corresponding to the licenseplate region. Finally, a fine image dividing unit removes the part thatdoes not correspond to the license plate from the rough license plateregion.

Moreover, in Taiwan Patent Pub. No. I286027, an integrated plurality oflane free flow vehicle enforcement system is disclosed, wherein a portalframed equipment is established at the image enforcement point. The carlane is physically divided so that image enforcement can be realizedwith respect to various cars even though the system slows the cars topass by the image enforcement point at a normal speed and to changelanes freely.

Moreover, in Taiwan Patent Appl. No. 200802137, a serial license plateidentification system is disclosed, using a license plate characterregion detection module to receive an image and determine eachapproximate license plate range in the image. Sequences of serialidentical pixels in each approximate license plate range are obtained.The sequences of serial identical pixels are erased, filtered, andconnected to blocks so as to obtain the image with respect to thelicense plate character region in each approximate license plate rangeand output verified image with respect to the license plate characterregion after verification. Then, the verified image with respect to thelicense plate character region is transmitted to the a license platecharacter dividing and identification module to acquire all theindependent character images and thus all the license plate characterinformation after the independent character images are identified.

Moreover, Taiwan Patent No. 221193 discloses a license plateidentification and monitoring apparatus used in a parking area. When acar passes by a predetermined image acquiring spot, the host is informedto enable the duplex image acquiring device to control the camera deviceto acquire the car license plate image, which is then processed by thean identification process to identify the characters on the licenseplate for car management, stolen car seeking and prevention in theparking area.

Taiwan Patent No. 226454 discloses a license plate identificationmethod, wherein the logic relation and character strokes are used todetermine the correct license plate location in the digital image. Then,ternarized difference and fuzzy inference are used to acquire theoutlines of the characters on the license plate. Adaptive binarizationmethod is used to divide the boundaries of each character. Finally, theidentification result can be obtained by a feature fused mediancalculation using a neural network.

Moreover, Taiwan Patent No. 191905 discloses a automatic mobile licenseplate identification system, which comprises an image acquiring deviceand an image processing device that can be installed in a car to performautomatic identification on a static or moving car being monitored. Theimage acquiring device is capable of acquiring the image of the licenseplate and transmitting the image into the image processing device. Theimage processing device performs a precise acquiring process on thelicense plate characters based on fuzzy inference and performs acharacter identification process on the characters using characterstructure analysis. Therefore, identification errors due to licenseplate contamination, bending, character contamination or deflexion canbe prevented.

Taiwan Patent No. 123259 discloses a license plate number identificationapparatus installed at a spot where cars pass by so as to automaticallyidentify the license plate number of a car. The license plate numberidentification apparatus uses an image acquiring device capable ofacquiring an image containing the license plate and an image processingunit capable of checking the digital image according to features of thelicense plate number to find the license plate location, specify therange of characters, divide the characters to achieve featureidentification of each characters.

Moreover, U.S. Pat. No. 4,817,166 discloses a method for reading alicense plate by acquiring the boundary features such as length, height,and width of the characters on the license plate. With such informationregarding the character features, geometric features of the characterssuch as the locations and shapes of convex hulls, turns and holes areanalyzed. Finally, the structure of each character on the license plateis analyzed according to the results of the aforegoing analysis.

Moreover, U.S. Pat. No. 6,553,131 discloses an identification technologyusing a smart image acquiring device to perform license plateidentification. A processor is installed inside the image acquiringdevice to perform license plate information identification. In thistechnology, image identification is implemented by determining a baseline according to the brightness and location of the license plate imageand a blur region. The image having a base line is then processed byprojection to obtain the location of each character on the licenseplate. A statistic-based method is used so that each character isprovided with a confidence index. Finally, character information on thelicense plate is determined according to the confidence index.

Moreover, U.S. Pat. No. 5,425,108 discloses a license plate imageidentification technology, wherein the acquired license plate image isprocessed by fuzzy interfere and the features of the license plate imageis identified by structure analysis using a neural network features.

Moreover, U.S. Pat. No. 6,473,517 discloses a license plateidentification technology, wherein the license plate image is identifiedby character segmentation. In this technology, the license plate imageis divided into a plurality of regions to be converted into possiblecharacter regions (or suspected character regions). Then, the possiblecharacter regions are identified to obtain a confidence index for imageidentification based thereon.

U.S. Pat. No. 5,081,685 discloses a license plate identificationtechnology, wherein image intensity information is used to identify thecharacters on the license plate. In this technology, the characters areseparated from the background on the license plate so as to obtain theoutlines of the characters by a tracking process.

SUMMARY OF THE INVENTION

The present invention provides a method for image recombination of aplurality of images and image identification and a system for imageacquiring and identification, wherein a plurality of imagescorresponding to a specific target are recombined to compensate theincomplete information of each image to form a recombined image withenhanced image identification.

The present invention provides a method for image recombination of aplurality of images and image identification and a system for imageacquiring and identification, wherein a recombined image formed byrecombining a plurality of images is identified with a plurality ofsimilarity indexes according to the provided information in a database.The possible results are obtained according to the similarity indexesfor the user to choose from with enhanced identification speed andprecision.

The present invention provides a method for image recombination of aplurality of images and image identification and a system for imageacquiring and identification, which is useful in identification on theidentification mark of a carrier. With enhanced character features ofthe identification mark, multi-angle license plate identificationtechnology can be used to help the user to identify cars that aresuspected to cause accidents.

In one embodiment, the present invention provides a method for imagerecombination of a plurality of images, comprising steps of: acquiring aplurality of images; determining a region of interest in an image fromthe plurality of images and acquiring features in the region ofinterest; acquiring from other regions a feature region corresponding tothe region of interest according to the acquired features; andperforming an image recombination process to form a recombined imageaccording to a plurality of feature regions and the region of interest.

In another embodiment, the present invention provides a method for imageidentification, comprising steps of: acquiring a plurality of images;determining a region of interest in an image from the plurality ofimages and acquiring features in the region of interest; acquiring fromother regions a feature region corresponding to the region of interestaccording to the acquired features; and performing an imagerecombination process to form a recombined image according to aplurality of feature regions and the region of interest; and identifyingthe recombined image.

In another embodiment, the present invention provides a system for imageacquiring and identification, comprising: an image input unit capable ofproviding a plurality of images; an image processing unit coupled to theimage input unit, the image processing unit further comprising: afeature acquiring unit capable of acquiring features in a region ofinterest on an image from the plurality of images and acquiring fromother regions a feature region corresponding to the region of interestaccording to the acquired features in other regions; a recombinationunit capable of performing an image recombination process according tothe plurality of feature regions and the region of interest to form arecombined image.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and spirits of the embodiments of the present invention willbe readily understood by the accompanying drawings and detaileddescriptions, wherein:

FIG. 1 is a flowchart of a method for image recombination of a pluralityof images according to one embodiment of the present invention;

FIG. 2A and FIG. 2B are schematic diagrams showing a car that is moving;

FIG. 3A and FIG. 3B are schematic diagrams showing the acquired imagesof a car at different locations;

FIG. 3C is a schematic diagram showing the angular relation with respectto the feature region and the region of interest;

FIG. 4A and FIG. 4B are schematic diagrams of a plurality of images anda recombined image respectively;

FIG. 5 is a flowchart of a method for image recombination of a pluralityof images according to another embodiment of the present invention;

FIG. 6A and FIG. 6B are schematic diagrams of a plurality of images anda recombined image respectively;

FIG. 7A and FIG. 7B show the greyscale value with respect to thelocation before and after histogram equalization respectively;

FIG. 8 is a flowchart of a method for image identification according tothe present invention;

FIG. 9A to FIG. 9D are schematic diagrams showing the formation of asample image;

FIG. 10A is a schematic diagram of a recombined image and a featureimage thereof;

FIG. 10B is a schematic diagram of a feature image;

FIG. 11 is a table for sorting the comparison results of anidentification mark according to the present invention; and

FIG. 12 is a schematic diagram of a system for image acquiring andidentification according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention can be exemplified but not limited by variousembodiments as described hereinafter.

Please refer to FIG. 1, which is a flowchart of a method for imagerecombination of a plurality of images according to one embodiment ofthe present invention. In the present embodiment, the method 2 startswith step 200 to acquiring a plurality of images. In the present step,the plurality of images can be acquired in many ways, for example,inputting a plurality of images or using a camera, a CCD or a CMOS imageacquiring unit, but not limited thereto, to acquire the plurality ofimages at different timings or using an image acquiring device toacquire images with sequential reation from continuous images by acamera. In FIG. 2A, when a car 90 is making a turn, images of the car 90are acquired at different timings by an image acquiring unit 25 toacquire a plurality of images. In FIG. 2B, images at different locationsare acquired when the car 90 is moving. For example, the image atlocation 91 is as shown in FIG. 3A, while the image at location 92 is asshown in FIG. 3B. The number of the images is based on the practicaldemand, and is thus not limited. In step 200, the images are images of acar, but not limited thereto.

Referring to FIG. 1, after the images are acquired, step 201 isperformed to load the plurality of images. Then, in step 212, the imagesare stored in a storage medium, such as a hard disk or memory. Then step202 is performed to choose from the plurality of images an image as astandard image. The chosen image can be the clearest one to be thestandard image. In the embodiment, the standard image is as shown inFIG. 3B. Then, step 203 is performed to determine a region of interest(ROI) in the standard image. The region of interest covers the licenseplate image of a car, as shown in region 93 in FIG. 3B. Then, step 204is performed to acquire the image features in the region of interest. Inthe present embodiment, the image features include contrast or greyscalevalue of the outlines of the word or digit on the license plate image inthe feature region.

Then, step 205 is performed to acquire from other regions a featureregion corresponding to the ROI according to the features acquired instep 204. The feature region is acquired manually or automatically withthe use of software. The feature region is determined according to theobject to be identified. In the present embodiment, an identificationmark of a car is used as an example. The feature region refers to aregion corresponding to the identification mark. Step 205 comprises twosteps as described herein. Firstly, the image stored in step 212 isread. Then, the features acquired in step 204 are loaded to perform afeature searching process on the loaded images. For example, a featureregion 94 can be searched from the image in FIG. 3A according tofeatures acquired in region 91 in FIG. 3B. In step 205, in addition toacquiring the feature region, the angular relation and the scalerelation between the feature region and the region of interest are alsodetermined. For example, in FIG. 3C, after the feature region 94 isacquired, step 25 further acquires a location 940 in the feature region94 corresponding to a spot 930 in the region of interest by geometricmatching. Then, according to the location 940, the angular relationbased on the coordinates of the feature region 94 is obtained.Furthermore, normalization can be achieved based on the angular relation(θ) and scale relation between the feature region 94 and the region ofinterest 93.

Referring to FIG. 1, in step 206, the feature region in other images isacquired repeatedly until the number of processed car images reaches thenumber of plurality of images. Afterwards, step 207 is performed tonormalize the acquired feature regions. The step of normalization isperformed according to the angular relation and the scale relation instep 205 so that the size of each feature region is adjusted to beidentical to the size of the region of interest or that the sizes of thefeature region and the region of interest are adjusted to a specificscale. Since there are a plurality of images acquired in step 200, thesize of the target (i.e., a car in the present embodiment) may vary, asshown in FIG. 3A and FIG. 3B, due to different view angles anddistances. As a result, the feature regions 93 and 94 acquired in step205 may differ in size. Therefore, step 207 is performed to adjust thesize of each feature region to be identical. In the present embodiment,the size is 130×130 pixels.

Then, step 208 performs an inversing operation on the pixels in theplurality of feature regions and the pixels in the region of interestrespectively. The inversing operation is achieved so that the brightturns dark and the dark turns bright. The sensitivity of human eyes issaturated in a bright environment, which leads to failure in identifyingthe detailed structure in the bright region. Therefore, it is easier toidentify the images after the inversing operation is performed to turnthe bright into dark. Afterwards, step 209 is performed to form arecombined image by summing the pixels in the plurality of featureregions and the pixels in the region of interest. Since the featureregion and the region of interest have been normalized to have the sameimage size, the greyscale values of the pixels can be summed. In step210, an inversing operation is performed on the recombined image. Then,step 211 is performed to enhance the recombined image. The image isenhanced to improve the contrast and the brightness. Referring to FIG.4A and FIG. 4B, FIG. 4A shows unidentified feature regions on aplurality of images (3 images in the present embodiment) beforerecombination while FIG. 4B is a schematic diagram of a recombined imageby the method shown in FIG. 1. In other words, the 3 images in FIG. 4Aare recombined to obtain a clear image.

Please refer to FIG. 5, which is a flowchart of a method for imagerecombination of a plurality of images according to another embodimentof the present invention. In the present embodiment, step 300 to step307 in the method 3 are similar to step 200 to step 207 in FIG. 1, anddescriptions thereof are thus not presented herein. The method 3 in thepresent embodiment is different from FIG. 1 in that the operation forobtaining the recombined image is different. After normalization in step307, step 308 is performed to average the feature regions in theplurality of images to obtain a recombined image. Each pixel in thefeature region and region of interest is summed and averaged to form therecombined image. Then, in step 309, a histogram equalization process isperformed on the recombined image. Th histeogram equalization process isaimed at enhancing the contrast of the recombined image. For example,FIG. 6A shows unidentified feature regions on a plurality of images (3images in the present embodiment) before recombination, wherein thefeatures on each image are blur and unclear. After step 308 and step 309Are performed, a clear image is formed as shown in FIG. 6B. FIG. 7A andFIG. 7B show the greyscale value with respect to the location before andafter histogram equalization respectively. It is observed that thecontrast difference d is small (FIG. 7A) before the histogramequalization process in step 309, while the contrast difference D islarge (FIG. 7B) after the histogram equalization process in step 309. Alarger contrast difference D (FIG. 7B) is helpful for imageidentification. Referring to FIG. 5, step 310 is performed to enhancethe features in the recombined image for image identification.

Please refer to FIG. 8, which is a flowchart of a method for imageidentification according to the present invention. The flowchart of themethod for image identification can be achieved using the recombinedimage formed in FIG. 1 or FIG. 5 to obtain identification results withrespect to the recombined image. In other words, the recombined imageformed in FIG. 1 or FIG. 5 is used to obtain information according tothe comparison with the plurality of sample images. The method 4 startswith step 40 to provide a database. The database provides a plurality ofstandard sample images. Please refer to FIG. 9A, which is a schematicdiagram of a sample image. The size of the sample image 5 is determinedaccording to the user's demand, for example, 130×130 pixels, but notlimited thereto. A standard image region 50 is formed on the pixel inthe sample image 5. The standard image region 50 comprises a pluralityof pixels 500 and 501 to form a character, a digit, a word or a patternas represented by the sample image. Referring to FIG. 9B, the presentembodiment is exemplified by a digit “1”. In the sample image 5, eachpixel 500 and 501 is given a proper greyscale value to form a standardimage region 50, which draws the outline of the digit 1. Then, in thestandard image region 50, specific pixels 501 (pixels with obliquelines) are given a specific weight value. The greyscale value and theweight value are determined according to the user's demand. That is,each weight value may be different or identical. In the presentembodiment, the weight value is positive. In the standard image region50, the greyscale value and the weight value for each pixel 500 and 501are combined as the first feature value.

In the sample image, the non-standard image region 51 is provided asshown in FIG. 9C. The non-standard image region 51 represents thecontent that the standard image region 50 is taken for. For example,digit “1” is often taken for letter “I” or “L” or even letter “E.Therefore, locations for pixels 510 possibly mis-identified (pixels withdots) are given proper greyscale values and weight values as the secondfeature values corresponding to pixels 510. In the present embodiment,locations for the pixels 510 in the non-standard image region 51 aredetermined according to the easily mis-identified character, digit orword in the standard image region 50, which is not restricted. Thegreyscale values and weight values are determined according to practicaldemand. In the present embodiment, the weight values in the non-standardimage region 51 are negative.

As shown in FIG. 9D, which is a schematic diagram showing another sampleimage 5 a provided according to digit 0, the sample image 5 a alsocomprises a standard image region and a non-standard image region. Thepattern constructed by the pixels in the standard image region draws theoutline of a digit “0”. Similarly, the pattern constructed by the pixelsin the non-standard image region denotes a word that digit “0” is takenfor. For example, digit “0” is often taken for letter “Q” or digit “8”.Steps 221 and 222 can be performed using image processing softwareexemplified by, but not limited to, MS Paint. The sample images, such as0 to 9, A to Z and a to z, are stored in the database. Then, in step224, the identification result is observed after a plurality times oftraining.

Referring to FIG. 8, step 41 is performed to acquire a feature imagefrom the recombined image. For example, in FIG. 10A, the region in therecombined image 95 (the image in FIG. 6B) corresponding to eachunidentified word is the feature image. In step 41, the acquired featureimage 96 is the first character in the identification information. Then,step 42 performs a calculation on a third feature value of each pixel inthe feature image and the first feature value or the second featurevalue corresponding to each pixel in the plurality of sample images toobtain a similarity index of the feature image corresponding to theplurality of sample images respectively.

Please refer to FIG. 10B, which is a schematic diagram showing a featureimage 96. The feature image can be processed with each of the sampleimages for further calculation to obtain a corresponding similarityindex C_(uv). The calculation is based on normalized correlationmatching, as described in equation (1). Normalized correlation matchingis aimed at calculating the relation between the feature image and thesample image, wherein the standard deviation of the greyscale value ofeach image is regarded as a vector and is multiplied with the weightvalue so as to determine the optimal location. The standard correlationvalue is within the range between −1 and 1 with higher similarity as itgets closer to 1. When C_(uv) reaches its maximum, an optimal locationis achieved.

$\begin{matrix}{C_{uv} = \frac{\sum{\left( {u_{i} - \overset{\_}{u}} \right)\left( {v_{i} - \overset{\_}{v}} \right) \times w_{i}}}{\left\lbrack {\sum{\left( {u_{i} - \overset{\_}{u}} \right)^{2}{\sum\left( {v_{i} - \overset{\_}{v}} \right)^{2}}}} \right\rbrack^{1/2}}} & (1)\end{matrix}$

wherein u_(i) is the greyscale value of each pixel in the sample image,while v_(i) is the greyscale value of each pixel in the feature image,i.e., the third feature value. Moreover, ū is the average greyscalevalue of all the pixels in the sample image, while v is the averagegreyscale value of all the pixels in the feature image. w_(i) is theweight value of the pixels in the standard image region and thenon-standard image region in the sample image. The weight value ofpixels in the other region is 1.

Based on equation (1), a calculation is performed on each pixel in FIG.10B and each pixel in the sample image. For example, FIG. 10B and thesample image in FIG. 9C (representing digit 1) and the sample image inFIG. 9D (representing digit 0) are calculated to obtain the similarityindex C_(uv) of the feature image in FIG. 10B corresponding to FIG. 9Cand FIG. 9D. Referring to FIG. 8, after obtaining the similarity index,steps 43 and 44 are performed to acquire the feature image from eachcharacter in the recombined image 95. Step 42 is then repeated toperform identification. Step 45 collects a plurality of similarityindexes with respect to the feature image compared with the plurality ofsample images. In the present step, the similarity indexes are sortedfrom the identification result with highest possibility to theidentification result with lowest possibility. Finally, in step 46, theplurality of similarity indexes are sorted and at least one ofcomparison results is output.

Referring to FIG. 3B, since there are 7 characters in the identificationmark, the result shown in FIG. 11 can be obtained after the flowchart ofthe method 4 for identification is performed. In FIG. 11, four possibleresults are shown. Each result represents one possible combination ofcharacters on the license plate. Each character in the first possibleresult has the highest similarity, which is followed by the second, thethird and the fourth possible results. Taking the first possible resultfor example, the characters on the license plate are possibly 6095-OA,wherein the first character “6” has a similarity index of 72, the secondcharacter “0” has a similarity index of 52, the third character “9” hasa similarity index of 67, the fourth character “5” has a similarityindex of 72, the fifth character is “-”, sixth character “O” has asimilarity index of 63, and the seventh character “A” has a similarityindex of 76. Certainly, the user can also determine other combinationsof characters on the license plate number according to the results inFIG. 11 and visual estimation on the image to be identified.

In image identification, images of impossible characters or digits canbe excluded according to various combinations that form theidentification marks. For example, in one embodiment, the identificationmark can be formed as a combination of 4 leading digits and 2 followingletters (as shown in FIG. 3A) with a “-” therebetween. In anotheridentification mark, 2 leading letters and 4 follwing digits arecombined, with a “-” therebetween. In the present embodiment, there aretwo kinds of combinations to exemplify the license plates. Therefore,images of impossible characters or digits can be excluded according tothe relative locations of the feature images in the identification markso as to increase identification efficiency.

Please refer to FIG. 12, which is a schematic diagram of a system forimage acquiring and identification according to the present invention.The system 6 is capable of implementing the flowchart in FIG. 1, FIG. 5or FIG. 8 for image identification and identification result output. Thesystem 6 comprises a database 60, an image processing unit 61, anidentification and output unit 62, a plurality of image acquiring units63 and an image input unit 64. The database 60 is capable of providing aplurality of sample images. The plurality of image acquiring units 63are electrically connected to the image processing unit 61. Each imageacquiring unit 63 is capable of acquiring an image of an object andtransmits the image to the image processing unit 61 for identification.In the present embodiment, each of the image acquiring units 63 iscapable of acquiring dynamic or static images of the object. The imageprovides an identification region for carrier identification. Theidentification region comprises identification information. The imageacquiring units may be CCD or CMOS image acquiring units, but notlimited thereto. The object may be a carrier with an identification markthereon, for example, the license plate number of a car. Moreover, theobject may also be a word, a character, a digit or combinations thereof.

The image input unit 64 is capable of receiving and transmitting theplurality of images acquired by the image acquiring unit 63 to the imageprocessing unit 61. The image processing unit 61 comprises a featureacquiring unit 610, a recombination unit 611, an enhancing unit 612 andan identification and comparison unit 613. The feature acquiring unit610 is capable of acquiring features in the region of interest in astandard image and acquiring a feature region corresponding to theregion of interest according to acquired features in other regions. Thestandard image is formed by choosing one image from the plurality ofimages. The recombination unit 611 performs an image recombinationprocess according to the plurality of feature regions and the region ofinterest to form a recombined image. The recombined image is formed asdisclosed in FIG. 1 or FIG. 5. The enhancing unit 612 is capable ofimproving the recombined image to enhance the contrast, brightness orthe edge features of the recombined image.

The identification and comparison unit 4111 performs step 23 in FIG. 1to compare the feature image with the sample image to obtain theplurality of similarity indexes corresponding thereto, and sorts theplurality of similarity indexes to output at least one of comparisonresults. The identification and output unit 42 is electrically connectedto the processing unit 41 to output the comparison result identified bythe processing unit 41. The output from the identification and outputunit 42 is as shown in FIG. 8A, which is capable of allowing the user toknow the identification results displayed on a display.

The identification and comparison unit 613 is electrically connected tothe enhancing unit 612 to identify the recombined image. Theidentification and comparison unit 613 performs a calculation on eachpixel in the feature image and each pixel in the plurality of sampleimages to obtain a similarity index of the feature image correspondingto the plurality of sample images respectively according to theflowchart in FIG. 8, and further collects a plurality of similarityindexes with respect to the feature image compared with the plurality ofsample images. The output unit 62 is electrically connected to theprocessing unit 61 to output at least one of comparison results from theprocessing unit 61.

Accordingly, the present invention discloses a method for imageprocessing and identification and a system for image acquiring andidentification with enhanced efficiency and precision. Therefore, thepresent invention is useful, novel and non-obvious.

Although this invention has been disclosed and illustrated withreference to particular embodiments, the principles involved aresusceptible for use in numerous other embodiments that will be apparentto persons skilled in the art. This invention is, therefore, to belimited only as indicated by the scope of the appended claims.

1. A method for image recombination of a plurality of images, comprisingsteps of: acquiring a plurality of images; determining a region ofinterest in an image from the plurality of images and acquiring featuresin the region of interest; acquiring from other regions a feature regioncorresponding to the region of interest according to the acquiredfeatures; and performing an image recombination process to form arecombined image according to a plurality of feature regions and theregion of interest.
 2. The method for image recombination of a pluralityof images as recited in claim 1, further comprising a step of performingan image enhancing process on the recombined image.
 3. The method forimage recombination of a plurality of images as recited in claim 1,wherein the image recombination process comprises steps of: performingan operation process on corresponding pixels in the plurality of featureregions and the region of interest; and forming the recombined imageafter the operation process.
 4. The method for image recombination of aplurality of images as recited in claim 3, wherein the correspondingpixels are averaged in the operation process.
 5. The method for imagerecombination of a plurality of images as recited in claim 4, furthercomprising a step of performing a histogram equalization process on therecombined image.
 6. The method for image recombination of a pluralityof images as recited in claim 3, wherein the operation process furthercomprises steps of: performing an inversing operation on the pixels inthe plurality of feature regions and the pixels in the region ofinterest respectively; summing the pixels in the plurality of featureregions and the pixels in the region of interest to form the recombinedimage; and performing an inversing operation on the recombined image. 7.The method for image recombination of a plurality of images as recitedin claim 1, wherein the region of interest is determined by choosingfrom the plurality of images an image as a standard image and choosingfrom the standard image a region as the region of interest.
 8. Themethod for image recombination of a plurality of images as recited inclaim 1, further comprising a step of normalizing the plurality ofimages.
 9. A method for image identification, comprising steps of:acquiring a plurality of images; determining a region of interest in animage from the plurality of images and acquiring features in the regionof interest; acquiring from other regions a feature region correspondingto the region of interest according to the acquired features; andperforming an image recombination process to form a recombined imageaccording to a plurality of feature regions and the region of interest;and identifying the recombined image.
 10. The method for imageidentification as recited in claim 9, wherein the step of identifyingthe recombined image further comprises steps of: acquiring at least onefeature image in the recombined image; providing a database comprising aplurality of sample images therein, each having respectively a standardimage region and at least a non-standard image region, wherein thestandard image region has pixels corresponding to a first feature valuerespectively, and the non-standard image region has pixels correspondingto a second feature value respectively; performing a calculation on athird feature value of each pixel in the feature image and the firstfeature value or the second feature value corresponding to each pixel inthe plurality of sample images to obtain a similarity index of thefeature image corresponding to the plurality of sample imagesrespectively; collecting a plurality of similarity indexes with respectto the feature image compared with the plurality of sample images; andsorting the plurality of similarity indexes and outputting at least oneof comparison results.
 11. The method for image identification asrecited in claim 10, wherein the calculation is based on normalizedcorrelation matching.
 12. The method for image identification as recitedin claim 10, further comprising a step of normalizing the feature imageto adjust the size and the angle of the feature image so that the sizeof the feature image is identical to the size of the sample image afteracquiring the feature image from the image.
 13. The method for imageidentification as recited in claim 10, wherein each sample imagecorresponds to an image of a digit or a character.
 14. The method forimage identification as recited in claim 10, wherein the first featurevalue and the second feature value are respectively a combination of aweight value and a greyscale value, and the third feature value is agreyscale value.
 15. The method for image identification as recited inclaim 9, wherein the recombined image comprises information regarding anidentification mark on a carrier.
 16. The method for imageidentification as recited in claim 9, wherein the image recombinationprocess comprises steps of: performing an operation process oncorresponding pixels in a plurality of feature regions and the region ofinterest; and forming the recombined image after the operation process.17. The method for image identification as recited in claim 16, whereinthe corresponding pixels are averaged in the operation process.
 18. Themethod for image identification as recited in claim 17, furthercomprising a step of performing a histogram equalization process on therecombined image.
 19. The method for image identification as recited inclaim 16, wherein the operation process further comprises steps of:performing an inversing operation on the pixels in the plurality offeature regions and the pixels in the region of interest respectively;summing the pixels in the plurality of feature regions and the pixels inthe region of interest to form the recombined image; and performing aninversing operation on the recombined image.
 20. A system for imageacquiring and identification, comprising: an image input unit capable ofproviding a plurality of images; an image processing unit coupled to theimage input unit, the image processing unit further comprising: afeature acquiring unit capable of acquiring features in a region ofinterest on an image from the plurality of images and acquiring fromother regions a feature region corresponding to the region of interestaccording to the acquired features in other regions; a recombinationunit capable of performing an image recombination process according tothe plurality of feature regions and the region of interest to form arecombined image.
 21. The system for image acquiring and identificationas recited in claim 20, wherein the image processing unit furthercomprises: a database comprising a plurality of sample images therein,each having respectively a standard image region and at least anon-standard image region, wherein the standard image region has pixelscorresponding to a first feature value respectively, and thenon-standard image region has pixels corresponding to a second featurevalue respectively; and an identification and comparison unit coupled tothe recombination unit to identify the recombined image, theidentification and comparison unit performing a calculation on eachpixel in the feature image and each pixel in the plurality of sampleimages to obtain a similarity index of the feature image correspondingto the plurality of sample images respectively, the identification andcomparison unit further collecting a plurality of similarity indexeswith respect to the feature image compared with the plurality of sampleimages.
 22. The system for image acquiring and identification as recitedin claim 20, wherein the image processing unit is further connected toan output unit to sort the plurality of similarity indexes and output atleast one of comparison results.
 23. The system for image acquiring andidentification as recited in claim 20, wherein the recombination unit isfurther connected to an enhancing unit to enhance the recombined image.24. The system for image acquiring and identification as recited inclaim 20, wherein the recombination unit is capable of normalizing theplurality of images for an image recombination process.